{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/13333"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/13333","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Using GIS to evaluate the impact of the built environment on health in “Brown’s Farm” Philippi","abstract":"This thesis uses a spatial epidemiology approach to investigate clusters of diseases across formal and informal built environments in \"Brown’s Farm\" Philippi. Health data were analysed using cluster detection methods in geographic information systems to identify diseases hotspots. The identified clusters were then examined against environmental, spatial, and socio-economic variables. Data from the Desmond Tutu HIVIAIDS Foundation database, census and questionnaires were used. 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Health data were analysed using cluster detection methods in geographic information systems to identify diseases hotspots. The identified clusters were then examined against environmental, spatial, and socio-economic variables. Data from the Desmond Tutu HIVIAIDS Foundation database, census and questionnaires were used. Even though most of the disease clusters were found in the informal part of the study area, the data showed very limited variation in the distribution of diseases clusters across the study area."]},{"key":"dc:title","label":"Title","values":["Using GIS to evaluate the impact of the built environment on health in “Brown’s Farm” Philippi"]}]}],"canonical_facts":{"dc:contributor.advisor":["Smit, Julian"],"dc:creator":["Letoao, Nthoesele"],"dc:date.accessioned":["2015-07-03T08:28:31Z"],"dc:date.available":["2015-07-03T08:28:31Z"],"dc:date.issued":["2014"],"dc:description":["Includes bibliographical references."],"dc:description.abstract":["This thesis uses a spatial epidemiology approach to investigate clusters of diseases across formal and informal built environments in \"Brown’s Farm\" Philippi. Health data were analysed using cluster detection methods in geographic information systems to identify diseases hotspots. 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